Columbia University

Consolidated Edison Company of New York, Inc.

Consolidated Edison of New York, Inc.

David Waltz

Roger N Anderson

Albert Boulanger

Arthur Kressner

Leon L Wu

John A Johnson

Maggie Chow

Mark Mastrocinque

Marta Arias

Philip Gross

Hila Becker

Rebecca J Passonneau

Delfina Isaac

Axinia Radeva

Peter Hofmann

Phil Long

Ansaf Salleb-Aouissi

Matthew Koenig

Frank Dougherty

Cynthia Rudin

Haimonti Dutta

Phil Gross

Huang Bert

Steve Ierome

A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.

Machine learning for power grid

A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

System and method for grading electricity distribution network feeders susceptible to impending failure

Growing community of inventors

Princeton, NJ, United States of America

David Waltz